Project/Area Number  07555122 
Research Category 
GrantinAid for Scientific Research (A)

Section  試験 
Research Field 
System engineering

Research Institution  Tohoku University 
Principal Investigator 
SAWADA Yasuji Research Institute of Electorical Communication, Tohoku University Professor, 電気通信研究所, 教授 (80028133)

CoInvestigator(Kenkyūbuntansha) 
HAYAKAWA Yoshihiro Research Institute of Electorical Communication, Tohoku University Associate Res, 電気通信研究所, 助手 (20250847)
NAKAJIMA Koji Research Institute of Electorical Communication, Tohoku University Professor, 電気通信研究所, 教授 (60125622)

Project Fiscal Year 
1995 – 1996

Project Status 
Completed(Fiscal Year 1996)

Budget Amount *help 
¥9,900,000 (Direct Cost : ¥9,900,000)
Fiscal Year 1996 : ¥3,400,000 (Direct Cost : ¥3,400,000)
Fiscal Year 1995 : ¥6,500,000 (Direct Cost : ¥6,500,000)

Keywords  CRANN / Analog Memory / SDAM / Neural Networkt / Limit cycle / Feedback / CMOS / Asymmetric / アナログメモリ / ニューラルネットワーク / リミットサイクル / フィードバック / 非対称 
Research Abstract 
We have fabricated a new analog memory for integrated artificial neural networks. Several attempts have been made to develop a linear characteristics of floatinggate analog memories with feedback circuits. The learning chip has to have a large number of circuits. Therefore, we proposed a new analog memory has a simple design, a small area occupancy, a fast switching speed and an accurate linearity. To improve accurate linearity, we propose a new charge transfer process. The device has a tunnel junction (polySi/polySi oxide/polySi sandwich structure), a thinfilm transistor (TFT). The proposed operation is possible that the amounts of transferred charges are constant independent of the charge in storage capacitor. And we have studied properties of neural networks with local connections. Each neurons receives input from and send output to other neurons with a local neighborhood. First a method to analyze the dynamics of networks in continuoustime model based on the force derived from the equation of motion for neural networks instead of energy function of the system has been proposed. In continuoustime model, the dynamic behavior of network can easily be analyzed by using this method without solving differential equations for all initial conditions. By using this method, we investigated experimentally the types of equilibrium points and limit cycles of continuoustime neural networks with cyclic and asymmetric connections and analyzed theoretically some types of equilibrium points and limit cycles. We also discussed the relation between the properties of limit cycles and the number of connections.
